Machine Learning

Machine learning is a rapidly growing and the most wanted technology in the field Computer Science and also in all sorts of organization and business. Machine learning involves learning about algorithms and statistical models, that are used by computers to undertake certain tasks depending on patterns, without the use of any instructions. 

Machine learning and data mining employs the same methods and overlap. Machine learning focuses on prediction based on known properties but data mining focuses on discovery  of unknown properties of data.


Key Features

  • Unsupervised Learning

         This algorithm takes a set of data that contains only inputs and find the structure in the data, like grouping or clustering of data points. The algorithms learn from test data that has not been characterized, identified or designated.

  • Clustering

    Cluster analysis is the combining of a set of observations into subsets (called clusters) so that observations that are in the same cluster are identical with respect to one or more preassigned standard, while observations that come from different clusters are not alike.

  • Regression

    This algorithm is used when the outputs are having any numerical value within the domain.

  • Classification

    Classification algorithm comes into use when there is a restriction on outputs to a limited set of values.

  • Testing data

         Once the model is trained, testing data provides an unbiased evaluation. When the inputs of testing data are fed, the model will predict some values. After prediction, the model will evaluate by comparing with the actual output present in the testing data.

  • Validation Data

    The part of data used to do frequent evaluation on the training data model.

  • Training Data

    This part of the data used for model training. Using this the inference is made from the model.

  • Statistics

          Machine learning and statistics are closely knitted. From the large data sets, using machine learning algorithms the patterns are predicted and the analysis is done using the statistical models.

  • Optimization

    Machine learning and optimization share common properties. They just differ in the goal of generalization. By optimization algorithms, the loss on a training set can be minimized. Machine learning is all about minimizing the loss on unseen samples.


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